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50 lines
2.2 KiB
50 lines
2.2 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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mobilenetv2 export file.
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"""
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import argparse
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import numpy as np
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from mindspore import Tensor, export, context
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from src.config import set_config
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from src.models import define_net, load_ckpt
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from src.utils import set_context
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parser = argparse.ArgumentParser(description="mobilenetv2 export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--batch_size", type=int, default=1, help="batch size")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--file_name", type=str, default="mobilenetv2", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
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parser.add_argument('--platform', type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"),
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help='run platform, only support GPU, CPU and Ascend')
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args = parser.parse_args()
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args.is_training = False
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args.run_distribute = False
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context.set_context(mode=context.GRAPH_MODE, device_target=args.platform)
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if args.platform == "Ascend":
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context.set_context(device_id=args.device_id)
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if __name__ == '__main__':
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cfg = set_config(args)
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set_context(cfg)
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_, _, net = define_net(cfg, args.is_training)
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load_ckpt(net, args.ckpt_file)
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input_shp = [args.batch_size, 3, cfg.image_height, cfg.image_width]
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input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
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export(net, input_array, file_name=args.file_name, file_format=args.file_format)
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